Date
Publisher
arXiv
This study investigates the strategic and epistemically responsible
integration of AI-powered chatbots into physics teacher education by employing
a TPACK-guided SWOT framework across three structured learning activities.
Conducted within a university-level capstone course on innovative tools for
physics instruction, the activities targeted key intersections of
technological, pedagogical, and content knowledge (TPACK) through
chatbot-assisted tasks: simplifying abstract physics concepts, constructing
symbolic concept maps, and designing instructional scenarios. Drawing on
participant reflections, classroom artifacts, and iterative feedback, the
results highlight internal strengths such as enhanced information-seeking
behavior, scaffolded pedagogical planning, and support for symbolic reasoning.
At the same time, internal weaknesses emerged, including domain-specific
inaccuracies, symbolic limitations (e.g., LaTeX misrendering), and risks of
overreliance on AI outputs. External opportunities were found in promoting
inclusive education, multilingual engagement, and expanded zones of proximal
development (ZPD), while external threats included prompt injection risks,
institutional access gaps, and cybersecurity vulnerabilities. By extending
existing TPACK-based models with constructs such as AI literacy,
prompt-crafting competence, and epistemic verification protocols, this research
offers a theoretically grounded and practically actionable roadmap for
embedding AI in STEM teacher preparation. The findings affirm that, when
critically scaffolded, AI chatbots can support metacognitive reflection,
ethical reasoning, and instructional innovation in physics education if
implementation is paired with digital fluency training and institutional
support.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
